In [2]:
from IPython.display import Javascript, display
from ipywidgets import widgets

def run_all(ev):
    display(Javascript('IPython.notebook.execute_cells_below()'))

button = widgets.Button(description="Click here to update tables + charts:")
button.on_click(run_all)
display(button)
In [3]:
#imports and pull from db
import pandas as pd
import numpy as np
import sqlalchemy as sa
import glob
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import plotly.plotly as py
from plotly.graph_objs import *
import datetime
%matplotlib inline  
#%pylab inline
pylab.rcParams['figure.figsize'] = (15, 25)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import qgrid
qgrid.nbinstall(overwrite=True) 

engine = sa.create_engine('mysql+pymysql://root:@localhost/rsi?charset=utf8')
conn = engine.connect()

#cds, cells, exp, fullexp, fullprocess, ivt, ivtdfmod, mods, modtrends, process, sencera, trends, xrf 

senceradf = pd.read_sql('sencera', conn, index_col="sencera_index")
processdf = pd.read_sql("process", conn, index_col="process_index")
expdf = pd.read_sql("exp", conn, index_col="exp_index")
celldf = pd.read_sql("cells", conn, index_col="cells_index")
moddf = pd.read_sql("mods", conn, index_col="mods_index")
trends = pd.read_sql("trends", conn, index_col="trends_index")
xrf = pd.read_sql("xrf", conn, index_col="xrf_index")
ivtdf = pd.read_sql("ivt", conn, index_col="ivt_index")
eddf = pd.read_sql("ed", conn, index_col="ed_index")

import time 
now = time.strftime("%c")
print ("Updated at: %s"  % now )

Updated at: Fri Jun  3 14:33:27 2016

trend charts

In [4]:
##module eff, FF, Voc, Jsc vs time
print("module trends:")

import seaborn as sns
flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("poster", font_scale=1.5,)

fg = sns.FacetGrid(moddf, hue='POR', size= 9, aspect=2.3)#, hue_kws=dict(marker=["^", "v"]))
fg.map(plt.scatter, 'Experiment_ID', 'percentEff', alpha=.7,)
plt.xlim(570,680)
plt.ylim(12, 17)
plt.axhline(15.5, color='k', linestyle='--')
plt.axhline(14.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
#plt.title('mod FOM')
plt.savefig('m_eff.png')

flatui1 = ["#95a5a6", "#9b59b6","#2ecc71", "#34495e", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)

fg1 = sns.FacetGrid(moddf, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'percentFF', alpha=.7)
plt.xlim(570,680)
plt.ylim(50, 80)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('m_ff.png')

flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

fg2 = sns.FacetGrid(moddf, hue='POR', size=9, aspect=2.5)
fg2.map(plt.scatter, 'Experiment_ID', 'modJsc', alpha=.7,)
plt.xlim(570,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 5, 25)])
#plt.title("module Jsc")
plt.ylim(22, 29)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('m_jsc.png')

flatui1 = ["#e74c3c", "#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)

fg21 = sns.FacetGrid(moddf, hue='POR', size= 9, aspect=2.5)
fg21.map(plt.scatter, 'Experiment_ID', 'modVoc', alpha=.7, )
plt.xlim(570,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 5, 25)])
#plt.title("module Voc")
plt.ylim(.76, .86)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('m_voc.png')

flatui1 = ["#9b59b6","#3498db","#e74c3c", "#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
fg3 = sns.FacetGrid(moddf, hue='POR', size= 9, aspect=2.3)
fg3.map(plt.scatter, 'Experiment_ID', 'modRs', alpha=.7,)
plt.xlim(560,680)
plt.ylim(.6, 4)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('m_rs.png')

flatui1 = [ "#e74c3c","#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
fg31 = sns.FacetGrid(moddf, hue='POR', size= 9, aspect=2.3)
fg31.map(plt.scatter, 'Experiment_ID', 'modRsh', alpha=.7,)
plt.xlim(560,680)
plt.ylim(1e1, 1e5)
plt.yscale('log')
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('m_rsh.png')

module trends:
In [5]:
##cells eff, FF, Voc, Jsc vs time
print("cell trends:")
import seaborn as sns
flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("poster", font_scale=1.5,)

fg = sns.FacetGrid(celldf, hue='POR', size= 9, aspect=2.3)#, hue_kws=dict(marker=["^", "v"]))
fg.map(plt.scatter, 'Experiment_ID', 'percentEff', alpha=.7,)
plt.xlim(570,680)
plt.ylim(12, 17)
plt.axhline(15.5, color='k', linestyle='--')
plt.axhline(14.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
#plt.title('cell FOM')
plt.savefig('c_eff.png')

flatui1 = ["#95a5a6", "#9b59b6","#2ecc71", "#34495e", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)

fg1 = sns.FacetGrid(celldf, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'percentFF', alpha=.7)
plt.xlim(570,680)
plt.ylim(50, 80)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('c_ff.png')

flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

fg2 = sns.FacetGrid(celldf, hue='POR', size=9, aspect=2.5)
fg2.map(plt.scatter, 'Experiment_ID', 'cellJsc', alpha=.7,)
plt.xlim(570,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 5, 25)])
#plt.title("cell Jsc")
plt.ylim(22, 29)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('c_jsc.png')

flatui1 = ["#e74c3c", "#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)

fg21 = sns.FacetGrid(celldf, hue='POR', size= 9, aspect=2.5)
fg21.map(plt.scatter, 'Experiment_ID', 'cellVoc', alpha=.7, )
plt.xlim(570,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 5, 25)])
#plt.title("module Voc")
plt.ylim(.76, .86)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('c_voc.png')

flatui1 = ["#9b59b6","#3498db","#e74c3c", "#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
fg3 = sns.FacetGrid(celldf, hue='POR', size= 9, aspect=2.3)
fg3.map(plt.scatter, 'Experiment_ID', 'cellRs_norm', alpha=.7,)
plt.xlim(560,680)
plt.ylim(0,20.)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('c_rs.png')

flatui1 = [ "#e74c3c","#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
fg31 = sns.FacetGrid(celldf, hue='POR', size= 9, aspect=2.3)
fg31.map(plt.scatter, 'Experiment_ID', 'cellRsh_norm', alpha=.7,)
plt.xlim(560,680)
plt.ylim(1e1, 1e8)
plt.yscale('log')
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('c_rsh.png')

cell trends:
In [6]:
print("latest 30 experiments, cell eff and ff data:")

import seaborn as sns
flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("poster", font_scale=1.5,)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper

# create our boxplot which is drawn on an Axes object
plt.figure(2, figsize=(20,12))
plt.subplot(211)
bplot = sns.boxplot('Experiment_ID', 'percentEff', hue='POR', data=celldf, notch=True)

# We can call all the methods avaiable to Axes objects
#bplot.set_title(title, fontsize=20)
#bplot.set_xlabel('Experiment ID', fontsize=16)
bplot.set_ylabel('cell efficiency [%]', fontsize=16)
bplot.tick_params(axis='y', labelsize=12, which='minor')
bplot.set_ylim(0,20)
bplot.set_xlim(378.5,408.5)
plt.xticks(rotation=30)
plt.legend()
plt.savefig('c_box_eff.png')

plt.subplot(212)
bplot22 = sns.boxplot('Experiment_ID', 'percentFF', data=celldf, hue='POR', notch=True)
bplot22.set_xlabel('Experiment ID', fontsize=16)
bplot22.set_ylabel('cell FF [%]', fontsize=16)
bplot22.tick_params(axis='both', labelsize=12)
bplot22.set_ylim(50,80)
bplot22.set_xlim(378.5,408.5)
plt.savefig('c_box_ff.png')

#sns.despine(left=True) 
plt.xticks(rotation=30)
#plt.legend()
plt.show()

latest 30 experiments, cell eff and ff data:
In [7]:
print("latest 30 experiments, cell jsc and voc data:")
sns.set(style='ticks')
flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
sns.set_context("poster", font_scale=1.5,)

plt.figure(2, figsize=(20,12))
plt.subplot(211)
bplot21 = sns.boxplot('Experiment_ID', 'cellJsc', hue='POR', data=celldf, notch=True)

#bplot.set_title(title, fontsize=20)
#bplot.set_xlabel('Experiment ID', fontsize=16)
bplot21.set_ylabel('Cell Jsc [$ma/cm^2$]', fontsize=16)
bplot21.tick_params(axis='both', labelsize=12)
bplot21.set_ylim(22,30)
bplot21.set_xlim(355.5,405.5)
plt.xticks(rotation=30)
#bplot.legend()
plt.savefig('c_box_j.png')

plt.subplot(212)
bplot2 = sns.boxplot('Experiment_ID', 'cellVoc', data=celldf, hue='POR', notch=True)
bplot2.set_xlabel('Experiment ID', fontsize=16)
bplot2.set_ylabel('Cell Voc [V]', fontsize=16)
bplot2.tick_params(axis='both', labelsize=12)
bplot2.set_ylim(0.6,0.9)
bplot2.set_xlim(355.5,405.5)
plt.savefig('c_box_v.png')


#sns.despine(left=True) 
plt.xticks(rotation=30)
plt.legend(loc='lower right')
plt.show()

latest 30 experiments, cell jsc and voc data:

metrology

In [8]:
cds = pd.DataFrame(trends[trends.Cell_Count >= 22])
cds = cds[~cds['Measurement'].str.contains('.*Dark.*', na = False)]

cds = cds.replace([np.inf, -np.inf], np.nan)

cds = pd.merge(cds, xrf, left_on='Sample ID', right_on='Sample')

cds['CdTe_Tool'] = '1'
mask1 = cds["CdTe 2 Tool"].str.contains('.*TANK.*', na=False)
cds.loc[:,'CdTe_Tool'][mask1] = '2'

print("module trends table:")

module trends table:

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:10: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [31]:
#qgrid.show_grid(cds, show_toolbar=True, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 150})

grid = qgrid.QGridWidget(df=moddf,show_toolbar=True, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 150})
display(grid)
In [20]:
list(cds.columns.values)
Out[20]:
['Sample ID',
 'Experiment_ID',
 'Description',
 'Date (IVT measure)',
 'Split1',
 'Split2',
 'Split3',
 'Comments',
 'POR',
 'Experiment ID',
 'Pallet Number',
 'Clean Process Start',
 'Clean Operator',
 'Substrate Type',
 'Clean Process ID',
 'Clean POR',
 'Clean Comment',
 'CdS Process Start',
 'CdS Operator',
 'CdS Tool',
 'Target Material',
 'Target Date',
 'Recipe ID',
 'Deposition Time (sec)/No of pass',
 'CdS POR',
 'CdS Thickness (nm)',
 'CdS Comment',
 'CdS Anneal Process Start',
 'CdS Anneal Operator',
 'CdS Anneal Tool',
 'CdS Anneal Recipe',
 'CdS Anneal POR',
 'CdS Anneal Comment',
 'CdTe 1 Process Start',
 'CdTe Operator',
 'CdTe Tool',
 'CdTe Solution ID',
 'CdTe Starting Potential mV',
 'CdTe QRP mV',
 'CdTe Total Charge C',
 'CdTe Chuck Temp C',
 'CdTe Solution Temp C',
 'Current (mA)',
 'CdTe Flow Rate L per min',
 'CdTe Flow Setting L per min',
 'CdTe Thickness in Angstroms',
 'CdTe POR',
 'CdTe Comment',
 'CdTe 2 Process Start',
 'CdTe 2 Operator',
 'CdTe 2 Tool',
 'CdTe 2 Solution ID',
 'CdTe 2 Starting Potential mV',
 'CdTe 2 QRP mV',
 'CdTe 2 Total Charge C',
 'CdTe 2 Chuck Temp C',
 'CdTe 2 Solution Temp C',
 'CdTe 2 Current (mA)',
 'CdTe 2 Flow Rate L per min',
 'CdTe 2 Flow Setting L per min',
 'CdTe 2 Thickness in Angstroms',
 'CdTe 2 POR',
 'CdTe 2 Comment',
 'CdTe Anneal Process Start',
 'CdTe Anneal Operator',
 'CdTe Anneal Tool',
 'CdTe Anneal Recipe',
 'CdTe Anneal POR',
 'CdTe Anneal Comment',
 'CdCl2 Process Start',
 'CdCl2  Operator',
 'CdCl2 Anneal Tool',
 'CdCl2  Recipe',
 'CdCl2 POR',
 'CdCl2 Comment',
 'Cu Process Start',
 'Cu Operator',
 'Cu Spray Tool',
 'Cu Anneal Tool',
 'Cu Recipe',
 'DAE Solution Date',
 'Cu Stock Date',
 'Cu Diluted Date',
 'Cu POR',
 'Cu Comment',
 'ZnTe Process Start',
 'ZnTe Operator',
 'ZnTe Tool',
 'ZnTe recipe',
 'ZnTe POR',
 'ZnTe Comment',
 'P1 Process Start',
 'P1 Operator',
 'P1 Tool',
 'P1 recipe',
 'P1 POR',
 'P1 Comment',
 'Photo Process Start',
 'Photo Operator',
 'Photo Tool',
 'Photo Anneal Oven',
 'Exposure Tool',
 'Photo Recipe',
 'Photoresist Batch Date',
 'Developer Solution Date',
 'Photo POR',
 'Photo Comment',
 'P2 Process Start',
 'P2 Operator',
 'P2 Tool',
 'P2 recipe',
 'P2 POR',
 'P2 Comment',
 'Sputter Process Start',
 'Sputter Operator',
 'Sputter Tool',
 'Sputter Recipe ID',
 'Sputter POR',
 'Sputter Slot',
 'Sputter Comment',
 'ARC Recipe',
 'ARC POR',
 'ARC Notes',
 'Metal Anneal Process Start',
 'Metal Anneal Operator',
 'Metal Anneal Tool',
 'Metal Anneal Temp',
 'Metal Anneal Recipe',
 'Metal Anneal POR',
 'Metal Anneal Comment',
 'P3 Process Start',
 'P3 Operator',
 'P3 Tool',
 'P3 recipe',
 'P3 POR',
 'P3 Comment',
 'Edge Delete Process Start',
 'Edge Delete Operator',
 'Edge Delete Tool',
 'EdgeDelete recipe',
 'Edge Delete POR',
 'Edge Delete Comment',
 'Additional Process Name',
 'Additional Process Start',
 'Additional Process Operator',
 'Additional Process Tool',
 'Additional Process Notes',
 'Additional Process Comments',
 'Cell_Area_mm2',
 'Cell_Count',
 'Cell_no',
 'Comment',
 'Current_Step',
 'Date_tm',
 'Eff',
 'Efficiency',
 'FF',
 'FillFactor',
 'I0_nA',
 'Iph_mA',
 'Isc_mA',
 'Measurement',
 'NumCells',
 'Pmp_fit_ratio',
 'Pmp_mW',
 'R_squared',
 'Roc_Ohm',
 'Rs_Ohm',
 'Rsc_Ohm',
 'Rsh_Ohm',
 'Sample_ID',
 'Temp_C',
 'User',
 'Vmp_V',
 'Voc_V',
 'n',
 'percentEff',
 'percentFF',
 'Voc',
 'Jsc',
 'Rs',
 'Rsh',
 'Unnamed: 0',
 'Sample',
 'XRF.Date',
 'XRF.Calibration',
 'CdTe_Thickness',
 'CdTe_Density',
 'Cd_Concentration',
 'Cd_Peak',
 'Cd_Background',
 'Te_Concentration',
 'Te_Peak',
 'Te_Background',
 'SnO2_Thickness',
 'SnO2_Density',
 'SnO2_Peak',
 'SnO2_Background',
 'SNR_Cd',
 'SNR_Te',
 'SNR_SnO2',
 'CdTe_Tool']
In [25]:
cdstrimmed = pd.DataFrame(cds.drop(cds.columns[[9]], axis=1)
cdstrimmed.head()

  File "<ipython-input-25-9722e94b238b>", line 2
    cdstrimmed.head()
             ^
SyntaxError: invalid syntax
In [10]:
print("unfiltered module stats:")
cds.describe()

unfiltered module stats:
Out[10]:
Experiment_ID CdS Thickness (nm) CdTe Thickness in Angstroms Cell_Area_mm2 Cell_Count Cell_no Eff Efficiency FF FillFactor I0_nA Iph_mA Isc_mA NumCells Pmp_fit_ratio Pmp_mW R_squared Roc_Ohm Rs_Ohm Rsc_Ohm Rsh_Ohm Temp_C Vmp_V Voc_V n percentEff percentFF Voc Jsc Rs Rsh Unnamed: 0 CdTe_Thickness CdTe_Density Cd_Concentration Cd_Peak Cd_Background Te_Concentration Te_Peak Te_Background SnO2_Thickness SnO2_Density SnO2_Peak SnO2_Background SNR_Cd SNR_Te SNR_SnO2
count 8625.000000 8531.000000 7408.000000 8791.000000 8791.000000 8791.000000 0 8791.000000 0 8784.000000 0 0 8791.000000 0 0 8791.000000 0 8.791000e+03 8791.000000 8.791000e+03 8.789000e+03 8791.000000 8791.000000 8784.000000 0 8791.000000 8784.000000 8784.000000 8790.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8789.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000 8791.000000
mean 572.372290 69.068784 20101.164417 565.016449 22.000910 1.998862 NaN 0.116825 NaN 2.338636 NaN NaN 137.865139 NaN NaN 1452.392509 NaN 1.137151e+05 40676.693318 1.542215e+05 2.734260e+06 28.235784 12.378801 16.942101 NaN 11.682528 233.863597 0.770062 24.397803 5168.867173 7010.063237 4518.482425 19872.322546 7.276073 51.102389 4063.634740 175.565123 48.908738 3999.135934 265.446821 4351.094381 7.469914 1149.579684 101.400637 0.959482 0.936513 0.919123
std 58.842533 10.235833 4730.223552 7.394333 0.030155 0.033710 NaN 0.026594 NaN 161.985901 NaN NaN 15.567161 NaN NaN 330.764885 NaN 7.837842e+06 1346914.920039 8.738834e+06 2.385675e+08 2.015741 1.958370 1.823880 NaN 2.659362 16198.590065 0.082877 2.753232 356265.538929 397219.724361 1343.826664 3464.683013 0.032841 1.271837 611.441782 120.987322 1.036179 574.217739 119.841025 766.050398 0.552770 50.378863 30.289151 0.030437 0.034582 0.027105
min 467.000000 11.000000 1452.000000 0.000000 22.000000 1.000000 NaN 0.000000 NaN 0.087460 NaN NaN 0.000000 NaN NaN 0.000000 NaN 9.590290e-01 0.954137 1.665353e+00 6.095970e-01 22.406696 0.000000 0.032261 NaN 0.000000 8.746000 0.001466 0.000000 0.043592 0.075698 2250.000000 52.200000 6.887000 33.700000 12.000000 27.000000 25.100000 0.000000 108.000000 0.000000 7.300000 0.000000 54.000000 0.083922 0.000000 0.000000
25% 522.000000 61.000000 15871.000000 565.000000 22.000000 2.000000 NaN 0.109046 NaN 0.578125 NaN NaN 135.978709 NaN NaN 1355.235691 NaN 2.191802e+01 15.612176 1.110425e+03 1.868420e+03 26.631349 11.806377 16.897499 NaN 10.904600 57.812525 0.768068 24.045636 0.996274 50.473869 3364.000000 15653.000000 7.266000 50.700000 3376.000000 85.000000 48.700000 3351.000000 200.000000 3466.600000 7.300000 1125.000000 83.000000 0.944327 0.926145 0.910424
50% 573.000000 69.000000 21497.000000 565.000000 22.000000 2.000000 NaN 0.123980 NaN 0.629036 NaN NaN 140.478593 NaN NaN 1541.097392 NaN 2.457826e+01 17.910511 2.000999e+03 3.295951e+03 28.062357 12.926776 17.513635 NaN 12.398000 62.903550 0.796074 24.856465 1.117194 90.954497 4445.000000 21408.700000 7.274000 51.000000 4301.000000 127.000000 49.000000 4222.000000 243.000000 4846.400000 7.300000 1152.000000 92.000000 0.968148 0.937050 0.925421
75% 622.000000 72.000000 22456.000000 565.000000 22.000000 2.000000 NaN 0.133834 NaN 0.665850 NaN NaN 144.895775 NaN NaN 1664.275148 NaN 2.883159e+01 20.748376 4.341636e+03 6.899121e+03 29.979475 13.611598 17.817054 NaN 13.383400 66.585000 0.809864 25.649557 1.310527 197.347101 5688.000000 22386.900000 7.281000 51.300000 4486.000000 255.000000 49.300000 4405.000000 318.000000 4996.200000 7.300000 1172.000000 115.000000 0.977563 0.954113 0.932189
max 672.000000 112.000000 224018.000000 575.000000 23.000000 2.000000 NaN 0.164577 NaN 15182.410604 NaN NaN 171.986190 NaN NaN 2045.689848 NaN 7.277880e+08 71926771.933288 7.844196e+08 2.235724e+10 41.478232 18.102726 20.809271 NaN 16.457700 1518241.060400 0.945876 30.440034 33081270.805875 35655437.861323 6915.000000 32589.600000 8.650000 100.000000 9562.000000 2228.000000 66.300000 6176.000000 3426.000000 6850.000000 9.268000 1550.000000 706.000000 0.989588 0.979863 0.958365
In [ ]:
In [11]:
#uncomment below to send full trends df to excel


#from xlsxwriter.utility import xl_rowcol_to_cell
#writer_orig = pd.ExcelWriter('db.xlsx', engine='xlsxwriter')
#%time cds.to_excel(writer_orig, index=False, sheet_name='all')
#writer_orig.save()
#print('Exported to db.xlsx.')
In [12]:
sns.set_palette("husl")
sns.set_context("poster", font_scale=1.5,)
fg = sns.FacetGrid(cds, hue='CdS Tool', size= 9, aspect=2.4)#, col='CdS Tool')
fg.map(plt.scatter, 'Date_tm', 'CdS Thickness (nm)', alpha=.6,)
#plt.xlim(550,650)
plt.xlim([datetime.date(2015, 7, 1), datetime.date(2016, 7, 1)])
plt.ylim(20, 100)
#plt.axhline(15.5, color='k', linestyle='-')
plt.title("CdS thickness by tool")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend(loc="lower right")
plt.savefig('cds_thickvtime.png')


sns.set_palette("bright")
fg1 = sns.FacetGrid(cds, hue='CdS Tool', size= 9, aspect=2.4)#, col='CdS Tool')
fg1.map(plt.scatter, 'CdS Thickness (nm)', 'percentEff',alpha=.6,)
plt.xlim(50,100)
#plt.xlim([datetime.date(2015, 7, 1), datetime.date(2016, 7, 1)])
plt.ylim(10, 18)
plt.axhline(15.5, color='k', linestyle='-')
plt.title("CdS thickness v eff")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('cds_thickveff.png')
In [13]:
sns.set_context("poster", font_scale=1.5,)
fg = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg.map(plt.scatter,'Date_tm','Cd_Concentration', alpha=.6,)
#plt.xlim(50,54)
plt.xlim([datetime.date(2015, 12, 31), datetime.date(2016, 6, 25)])
plt.ylim(48, 54)
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.title("Cd conc")
plt.legend()
plt.savefig('cdte_cdvtime.png')

fg2 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg2.map(plt.scatter,'Date_tm','Te_Concentration', alpha=.6,)
#plt.xlim(50,54)
plt.xlim([datetime.date(2015, 12, 31), datetime.date(2016, 6, 25)])
plt.ylim(46, 50)
plt.title("Te conc")
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('cdte_teveff.png')

fg1 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg1.map(plt.scatter,'Cd_Concentration','percentEff', alpha=.6,)
plt.xlim(50,54)
plt.ylim(10, 17)
plt.title("Cd conc v. eff")
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('cdte_cdveff.png')

fg4 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg4.map(plt.scatter,'Cd_Concentration','Voc', alpha=.6,)
plt.xlim(50,54)
plt.ylim(0.7, 0.9)
plt.title("Cd conc v. voc")
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()
plt.savefig('cdte_cdvvoc.png')

stats:

In [14]:
cells = celldf.drop(['Eff', 'NumCells', 'FF', 'I0_nA', 'n', 'Pmp_fit_ratio'], axis=1)
cells = cells[~cells['Measurement'].str.contains('.*Dark.*', na = False)]
cells.head()


mods = moddf.drop(['Eff', 'NumCells', 'FF', 'I0_nA', 'n', 'Pmp_fit_ratio'], axis=1)
mods = mods[~mods['Measurement'].str.contains('.*Dark.*', na = False)]
mods.head()


groupmoddf = mods.groupby('Experiment_ID', as_index=False)
modmedian = groupmoddf.median()
modmean = groupmoddf.mean()


groupcelldf = cells.groupby('Experiment_ID', as_index=False)
cellmedian = groupcelldf.median()
cellmean = groupcelldf.mean()

print("cell median trends table:")

cell median trends table:
In [15]:
qgrid.show_grid(cellmedian, show_toolbar=True, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 100})
In [16]:
b = pd.DataFrame(moddf.groupby(['Experiment_ID'], as_index=False)['shunt_count'].sum())

b.sort_values(by='Experiment_ID')
b.head()
plt.figure(figsize=(15, 8))

sns.barplot(x='Experiment_ID', y='shunt_count', data=b)
plt.xlim(367.5, 407.5)
plt.ylim(0,30)
plt.title("mean shunted modules by exp")
#plt.xticks(rotation=30)
Out[16]:
<matplotlib.text.Text at 0x20cb16dc2e8>
In [ ]: